We use the R package pointblank
to review and validate the plot-level descriptors
(HDP_plots.csv) and clean demographic data set
(heliconia_data_clean.csv) in preparation for archiving in
Dryad and publication in Bruna et al. (2023). The report below
includes:
Tests to determine if columns are correctly coded as integer,
character, etc.
Test criteria: Strict (‘stop’ if any rows
fail).
| Pointblank Validation | |||||||||||||
| Data Validation
tibbleWARN
1
STOP
0.02
NOTIFY
—
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Height is measured to nearest cm
|
— |
|
✓ |
57K |
57K1.00 |
00.00 |
— |
○ |
— |
— | ||
| 2 | Shoots is interger
|
— |
|
✓ |
57K |
57K1.00 |
00.00 |
— |
○ |
— |
— | ||
| 3 | Number of inflorescences is integer
|
— |
|
✓ |
2K |
2K1.00 |
00.00 |
— |
○ |
— |
— | ||
| 2023-05-30 16:36:22 EDT < 1 s 2023-05-30 16:36:23 EDT | |||||||||||||
Test for any nonexistent values of plot_id (e.g.,
‘FF-10’, ‘CF-23’) or subplot (e.g., ‘H23’, ‘A11’).
Test criteria: Strict (‘stop’ if any rows
fail).
| Pointblank Validation | |||||||||||||
| Data Validation
tibbleWARN
1
STOP
0.02
NOTIFY
—
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | col_vals_in_set()
|
|
✓ |
67K |
67K1.00 |
00.00 |
— |
○ |
— |
— | |||
| 2 | col_vals_in_set()
|
|
✓ |
67K |
67K1.00 |
00.00 |
— |
○ |
— |
— | |||
| 2023-05-30 16:36:23 EDT < 1 s 2023-05-30 16:36:23 EDT | |||||||||||||
Tests for duplicated rows, missing plant_ID numbers, or
duplicate plant_id numbers (test is done for every survey
year).
Test criteria: Strict (‘stop’ if any rows
fail).
| Pointblank Validation | |||||||||||||
| Data Validation
tibbleWARN
1
STOP
0.02
NOTIFY
—
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | duplicated rows
|
— | — |
|
✓ |
67K |
67K1.00 |
00.00 |
— |
○ |
— |
— | |
| 2 | col_vals_not_null()
|
— |
|
✓ |
67K |
67K1.00 |
00.00 |
— |
○ |
— |
— | ||
| 3 | Check for duplicate plant ID numbers
|
— |
|
✓ |
9K |
9K1.00 |
00.00 |
— |
○ |
— |
— | ||
| 4 | Check for duplicate tag numbers in a plot
|
— |
|
✓ |
64 |
00.00 |
641.00 |
— |
● |
— |
|||
| 2023-05-30 16:36:24 EDT 3.1 s 2023-05-30 16:36:27 EDT | |||||||||||||
Tests to determine how many values of plant size (shts,
ht) or infloresence number (infl) are outside
the range of most values.
Test criteria: ‘warn’ if \(\geq\) 1 rows fail conditions, ‘stop’ if
\(\geq\) 2% of rows fail
conditions.
| Pointblank Validation | |||||||||||||
| Data Validation
tibbleWARN
1
STOP
0.02
NOTIFY
—
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | shoots between 0 and 20
|
|
✓ |
67K |
67K0.99 |
80.01 |
● |
○ |
— |
||||
| 2 | height between 0 and 200cm
|
|
✓ |
67K |
67K0.99 |
20.01 |
● |
○ |
— |
||||
| 3 | infloresences between 0 and 3
|
|
✓ |
67K |
67K0.99 |
150.01 |
● |
○ |
— |
||||
| 2023-05-30 16:36:27 EDT < 1 s 2023-05-30 16:36:27 EDT | |||||||||||||
Tests for unusual changes in plant size (both height and shoot
number) from \(Year_{t}\) to \(Year_{t+1}\).
Test criteria: ‘warn’ if \(\geq\) 1 rows fail conditions, ‘stop’ if
\(\geq\) 2% of rows fail
conditions.
| Pointblank Validation | |||||||||||||
| Check growth & regression
tibbleWARN
1
STOP
0.02
NOTIFY
—
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | |% change in height| < 200%
|
|
✓ |
67K |
66K0.99 |
4200.01 |
● |
○ |
— |
||||
| 2 | |∆ height| < 100cm
|
|
✓ |
67K |
67K0.99 |
110.01 |
— |
● |
— |
||||
| 3 | |∆ shoot number| < 5
|
|
✓ |
67K |
67K0.99 |
2010.01 |
— |
● |
— |
||||
| 2023-05-30 16:36:28 EDT < 1 s 2023-05-30 16:36:28 EDT | |||||||||||||
Tests for seedlings whose size at initial marking was unusually
large. Conducted for both height and shoot number.
Test criteria: ‘warn’ if \(\geq\) 1 rows fail conditions, ‘stop’ if
\(\geq\) 2% of rows fail
conditions.
| Pointblank Validation | |||||||||||||
| Check seedlings
tibbleWARN
1
STOP
0.02
NOTIFY
—
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | shoots < 3
|
|
✓ |
3K |
3K0.99 |
120.01 |
● |
○ |
— |
||||
| 2 | height < 30cm
|
|
✓ |
3K |
3K0.99 |
30.01 |
● |
○ |
— |
||||
| 2023-05-30 16:36:29 EDT < 1 s 2023-05-30 16:36:29 EDT | |||||||||||||
Graphical depiction of the proportion of plants in each demographic plot for which there is no measurement of plant height (e.g., if plant not found).
Zombie plants are those that were recorded as ‘Dead’ in a survey but
for which there is a measurement in a subsequent year (indicative of the
plant losing all below-ground parts and then new shoots emerging prior
to the next survey). This validation generates a .csv of
any plants meeting this condition (labeled as ’zombie` for review and
correction.
| Pointblank Validation | |||||||||||||
| Check for zombies
tibbleWARN
1
STOP
0.02
NOTIFY
—
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | S | N | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Check for Zombies
|
|
✓ |
0 |
0NA |
0NA |
— |
○ |
— |
— | |||
| 2023-05-30 16:36:31 EDT < 1 s 2023-05-30 16:36:31 EDT | |||||||||||||